WEIGHTED VOTING OF MULTIPLE ALN CLASSIFIERS FOR OCR
Dejan Gorgevik, Dragan Mihajlov
Abstract: This paper addresses the problem of combining the results of multiple adaptive logic networks that were used as classifiers in OCR system for Macedonian Cyrillic. Combining is performed by weighted voting of some or all the classifiers. The weights associated to every trained classifier are evaluated on a test sample. As a result of the weighted voting process, a list of character candidates along with their confidence levels is produced for every pattern. The confidence levels of the character candidates are used in the contextual postprocessing phase for word candidates generation and lexicon lookup.
Keywords: OCR, ALN, weighted voting, classifier combining, character shape preclassification